PREDICTIVE MODEL FOR EQUIPMENT MONITORING USING IOT SENSORS AND CLOUD-BASED AI

Abstract

This project focuses on developing a predictive model for equipment monitoring using IoT sensors and cloud-based AI technologies. The primary objective is to enhance operational efficiency and reliability by leveraging real-time data from sensors to predict equipment failures and optimize maintenance schedules. The project involves selecting and integrating IoT sensors, collecting and preprocessing sensor data, and employing machine learning algorithms to build predictive models. These models are trained using historical data and deployed on AWS Cloud for continuous monitoring and predictive analytics. The project emphasizes the importance of data preprocessing, model selection, and evaluation to ensure accurate predictions. By integrating AWS Cloud AI capabilities such as AWS Lookout for Equipment, AWS IoT Greengrass, and AWS Lambda, the project aims to enable proactive maintenance strategies, reduce downtime, and improve overall equipment performance in industrial settings. Key steps include selecting appropriate IoT sensors, collecting and integrating data into AWS Cloud, preprocessing and engineering features for modeling, selecting and training predictive algorithms using AWS SageMaker, and deploying models for real-time inference. By leveraging AWS Cloud's robust infrastructure and AI capabilities, this initiative contributes to smarter industrial operations and improved decision-making in maintenance strategies.